Minimum Excess Risk in Bayesian Learning-Reference-Cited by-同舟云学术

Minimum Excess Risk in Bayesian Learning

Published:2022-12 Issue:12 Volume:68 Page:7935-7955
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

Xu Aolin¹,Raginsky Maxim²^ORCID

Affiliation:

1. University of Illinois at Urbana-Champaign, Champaign, IL, USA

2. Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Funder

NSF CAREER Award

Illinois Institute for Data Science and Dynamical Systems (iDS2), an NSF HDR TRIPODS Institute

DARPA under the LwLL (Learning with Less Labels) Program

ARO MURI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Link

https://ieeexplore.ieee.org/ielam/18/9961124/9780255-aam.pdf

Reference66 articles.

1. Information-theoretic asymptotics of Bayes methods

2. Universal coding, information, prediction, and estimation

3. Foundations of Modern Probability

4. A minimax approach to supervised learning;farnia;Proc Conf Neural Inf Process Syst,2016

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